Overview

Dataset statistics

Number of variables25
Number of observations10000
Missing cells10933
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory599.8 B

Variable types

Categorical8
Numeric17

Alerts

Customer_ID has a high cardinality: 7108 distinct valuesHigh cardinality
Type_of_Loan has a high cardinality: 3865 distinct valuesHigh cardinality
Credit_History_Age has a high cardinality: 401 distinct valuesHigh cardinality
Annual_Income is highly overall correlated with Monthly_Inhand_Salary and 2 other fieldsHigh correlation
Monthly_Inhand_Salary is highly overall correlated with Annual_Income and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Interest_Rate and 2 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 4 other fieldsHigh correlation
Num_of_Loan is highly overall correlated with Outstanding_DebtHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Num_of_Delayed_Payment is highly overall correlated with Num_Bank_Accounts and 2 other fieldsHigh correlation
Changed_Credit_Limit is highly overall correlated with Payment_of_Min_AmountHigh correlation
Num_Credit_Inquiries is highly overall correlated with Interest_Rate and 2 other fieldsHigh correlation
Outstanding_Debt is highly overall correlated with Interest_Rate and 5 other fieldsHigh correlation
Amount_invested_monthly is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Monthly_Balance is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Credit_Mix is highly overall correlated with Delay_from_due_date and 2 other fieldsHigh correlation
Payment_of_Min_Amount is highly overall correlated with Delay_from_due_date and 3 other fieldsHigh correlation
Age has 465 (4.7%) missing valuesMissing
Annual_Income has 733 (7.3%) missing valuesMissing
Monthly_Inhand_Salary has 1497 (15.0%) missing valuesMissing
Num_of_Loan has 514 (5.1%) missing valuesMissing
Type_of_Loan has 1129 (11.3%) missing valuesMissing
Num_of_Delayed_Payment has 972 (9.7%) missing valuesMissing
Changed_Credit_Limit has 220 (2.2%) missing valuesMissing
Num_Credit_Inquiries has 215 (2.1%) missing valuesMissing
Credit_Mix has 2025 (20.2%) missing valuesMissing
Outstanding_Debt has 108 (1.1%) missing valuesMissing
Credit_History_Age has 880 (8.8%) missing valuesMissing
Payment_of_Min_Amount has 1163 (11.6%) missing valuesMissing
Amount_invested_monthly has 912 (9.1%) missing valuesMissing
Customer_ID is uniformly distributedUniform
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 448 (4.5%) zerosZeros
Num_of_Loan has 1023 (10.2%) zerosZeros
Delay_from_due_date has 108 (1.1%) zerosZeros
Num_of_Delayed_Payment has 146 (1.5%) zerosZeros
Num_Credit_Inquiries has 707 (7.1%) zerosZeros
Total_EMI_per_month has 1056 (10.6%) zerosZeros

Reproduction

Analysis started2023-03-14 15:52:04.698692
Analysis finished2023-03-14 15:53:05.321414
Duration1 minute and 0.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Customer_ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct7108
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size731.8 KiB
CUS_0xa3a1
 
5
CUS_0x21c8
 
5
CUS_0x4ff5
 
5
CUS_0x1e41
 
5
CUS_0x199b
 
4
Other values (7103)
9976 

Length

Max length10
Median length10
Mean length9.9393
Min length9

Characters and Unicode

Total characters99393
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4745 ?
Unique (%)47.4%

Sample

1st rowCUS_0x4662
2nd rowCUS_0xb2a1
3rd rowCUS_0x363d
4th rowCUS_0xc1e7
5th rowCUS_0x26a9

Common Values

ValueCountFrequency (%)
CUS_0xa3a1 5
 
0.1%
CUS_0x21c8 5
 
0.1%
CUS_0x4ff5 5
 
0.1%
CUS_0x1e41 5
 
0.1%
CUS_0x199b 4
 
< 0.1%
CUS_0x8715 4
 
< 0.1%
CUS_0xbc7d 4
 
< 0.1%
CUS_0x968a 4
 
< 0.1%
CUS_0x24e7 4
 
< 0.1%
CUS_0x9daa 4
 
< 0.1%
Other values (7098) 9956
99.6%

Length

2023-03-14T16:53:05.392566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cus_0xa3a1 5
 
< 0.1%
cus_0x4ff5 5
 
< 0.1%
cus_0x1e41 5
 
< 0.1%
cus_0x21c8 5
 
< 0.1%
cus_0x3f3e 4
 
< 0.1%
cus_0x44c5 4
 
< 0.1%
cus_0xa72c 4
 
< 0.1%
cus_0x802f 4
 
< 0.1%
cus_0x7dcd 4
 
< 0.1%
cus_0xaf5f 4
 
< 0.1%
Other values (7098) 9956
99.6%

Most occurring characters

ValueCountFrequency (%)
0 11829
11.9%
C 10000
 
10.1%
S 10000
 
10.1%
_ 10000
 
10.1%
x 10000
 
10.1%
U 10000
 
10.1%
2 2759
 
2.8%
4 2758
 
2.8%
7 2738
 
2.8%
6 2721
 
2.7%
Other values (11) 26588
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36088
36.3%
Uppercase Letter 30000
30.2%
Lowercase Letter 23305
23.4%
Connector Punctuation 10000
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11829
32.8%
2 2759
 
7.6%
4 2758
 
7.6%
7 2738
 
7.6%
6 2721
 
7.5%
9 2715
 
7.5%
3 2678
 
7.4%
5 2667
 
7.4%
8 2645
 
7.3%
1 2578
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
x 10000
42.9%
a 2692
 
11.6%
b 2671
 
11.5%
c 2191
 
9.4%
e 1973
 
8.5%
d 1922
 
8.2%
f 1856
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
C 10000
33.3%
S 10000
33.3%
U 10000
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53305
53.6%
Common 46088
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11829
25.7%
_ 10000
21.7%
2 2759
 
6.0%
4 2758
 
6.0%
7 2738
 
5.9%
6 2721
 
5.9%
9 2715
 
5.9%
3 2678
 
5.8%
5 2667
 
5.8%
8 2645
 
5.7%
Latin
ValueCountFrequency (%)
C 10000
18.8%
S 10000
18.8%
x 10000
18.8%
U 10000
18.8%
a 2692
 
5.1%
b 2671
 
5.0%
c 2191
 
4.1%
e 1973
 
3.7%
d 1922
 
3.6%
f 1856
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11829
11.9%
C 10000
 
10.1%
S 10000
 
10.1%
_ 10000
 
10.1%
x 10000
 
10.1%
U 10000
 
10.1%
2 2759
 
2.8%
4 2758
 
2.8%
7 2738
 
2.8%
6 2721
 
2.7%
Other values (11) 26588
26.8%

Month
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5151
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:05.523202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2948853
Coefficient of variation (CV)0.50826899
Kurtosis-1.2430159
Mean4.5151
Median Absolute Deviation (MAD)2
Skewness-0.0059985653
Sum45151
Variance5.2664986
MonotonicityNot monotonic
2023-03-14T16:53:05.633749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1283
12.8%
7 1269
12.7%
8 1268
12.7%
6 1253
12.5%
1 1244
12.4%
4 1236
12.4%
5 1225
12.2%
2 1222
12.2%
ValueCountFrequency (%)
1 1244
12.4%
2 1222
12.2%
3 1283
12.8%
4 1236
12.4%
5 1225
12.2%
6 1253
12.5%
7 1269
12.7%
8 1268
12.7%
ValueCountFrequency (%)
8 1268
12.7%
7 1269
12.7%
6 1253
12.5%
5 1225
12.2%
4 1236
12.4%
3 1283
12.8%
2 1222
12.2%
1 1244
12.4%

Age
Real number (ℝ)

Distinct232
Distinct (%)2.4%
Missing465
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean118.18028
Minimum-500
Maximum8698
Zeros0
Zeros (%)0.0%
Negative80
Negative (%)0.8%
Memory size156.2 KiB
2023-03-14T16:53:05.779639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-500
5-th percentile16
Q124
median33
Q342
95-th percentile54
Maximum8698
Range9198
Interquartile range (IQR)18

Descriptive statistics

Standard deviation726.42372
Coefficient of variation (CV)6.1467421
Kurtosis83.818823
Mean118.18028
Median Absolute Deviation (MAD)9
Skewness8.9366764
Sum1126849
Variance527691.42
MonotonicityNot monotonic
2023-03-14T16:53:05.918710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 303
 
3.0%
28 298
 
3.0%
38 292
 
2.9%
35 290
 
2.9%
32 290
 
2.9%
27 288
 
2.9%
30 280
 
2.8%
22 280
 
2.8%
20 277
 
2.8%
31 273
 
2.7%
Other values (222) 6664
66.6%
(Missing) 465
 
4.7%
ValueCountFrequency (%)
-500 80
 
0.8%
14 105
 
1.1%
15 165
1.7%
16 145
1.5%
17 138
1.4%
18 215
2.1%
19 255
2.5%
20 277
2.8%
21 265
2.6%
22 280
2.8%
ValueCountFrequency (%)
8698 1
< 0.1%
8697 1
< 0.1%
8682 1
< 0.1%
8678 1
< 0.1%
8666 1
< 0.1%
8663 1
< 0.1%
8623 1
< 0.1%
8552 1
< 0.1%
8470 1
< 0.1%
8442 1
< 0.1%

Occupation
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size717.0 KiB
_______
713 
Doctor
 
656
Mechanic
 
652
Scientist
 
647
Teacher
 
640
Other values (11)
6692 

Length

Max length13
Median length10
Mean length8.424
Min length6

Characters and Unicode

Total characters84240
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row_______
2nd rowDeveloper
3rd rowArchitect
4th rowJournalist
5th rowMedia_Manager

Common Values

ValueCountFrequency (%)
_______ 713
 
7.1%
Doctor 656
 
6.6%
Mechanic 652
 
6.5%
Scientist 647
 
6.5%
Teacher 640
 
6.4%
Architect 640
 
6.4%
Journalist 634
 
6.3%
Engineer 633
 
6.3%
Entrepreneur 618
 
6.2%
Accountant 614
 
6.1%
Other values (6) 3553
35.5%

Length

2023-03-14T16:53:06.103422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
713
 
7.1%
doctor 656
 
6.6%
mechanic 652
 
6.5%
scientist 647
 
6.5%
teacher 640
 
6.4%
architect 640
 
6.4%
journalist 634
 
6.3%
engineer 633
 
6.3%
entrepreneur 618
 
6.2%
accountant 614
 
6.1%
Other values (6) 3553
35.5%

Most occurring characters

ValueCountFrequency (%)
e 11091
13.2%
r 8620
10.2%
n 7433
 
8.8%
a 6726
 
8.0%
c 6332
 
7.5%
t 6297
 
7.5%
i 6204
 
7.4%
_ 5601
 
6.6%
o 3143
 
3.7%
M 3032
 
3.6%
Other values (18) 19761
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68742
81.6%
Uppercase Letter 9897
 
11.7%
Connector Punctuation 5601
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11091
16.1%
r 8620
12.5%
n 7433
10.8%
a 6726
9.8%
c 6332
9.2%
t 6297
9.2%
i 6204
9.0%
o 3143
 
4.6%
u 2443
 
3.6%
h 1932
 
2.8%
Other values (8) 8521
12.4%
Uppercase Letter
ValueCountFrequency (%)
M 3032
30.6%
A 1254
12.7%
E 1251
12.6%
D 1239
12.5%
S 647
 
6.5%
T 640
 
6.5%
J 634
 
6.4%
L 613
 
6.2%
W 587
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 5601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78639
93.4%
Common 5601
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11091
14.1%
r 8620
11.0%
n 7433
9.5%
a 6726
 
8.6%
c 6332
 
8.1%
t 6297
 
8.0%
i 6204
 
7.9%
o 3143
 
4.0%
M 3032
 
3.9%
u 2443
 
3.1%
Other values (17) 17318
22.0%
Common
ValueCountFrequency (%)
_ 5601
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11091
13.2%
r 8620
10.2%
n 7433
 
8.8%
a 6726
 
8.0%
c 6332
 
7.5%
t 6297
 
7.5%
i 6204
 
7.4%
_ 5601
 
6.6%
o 3143
 
3.7%
M 3032
 
3.6%
Other values (18) 19761
23.5%

Annual_Income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6793
Distinct (%)73.3%
Missing733
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean175577.01
Minimum7006.035
Maximum24065688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:06.259921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7006.035
5-th percentile9897.8755
Q119357.89
median37006.6
Q371676.795
95-th percentile133439.76
Maximum24065688
Range24058682
Interquartile range (IQR)52318.905

Descriptive statistics

Standard deviation1423217.9
Coefficient of variation (CV)8.1059465
Kurtosis169.85218
Mean175577.01
Median Absolute Deviation (MAD)20892.355
Skewness12.665067
Sum1.6270722 × 109
Variance2.0255491 × 1012
MonotonicityNot monotonic
2023-03-14T16:53:06.459185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109945.32 5
 
0.1%
32486.64 5
 
0.1%
31757.98 5
 
0.1%
59692.58 4
 
< 0.1%
22246.44 4
 
< 0.1%
32625.59 4
 
< 0.1%
29346.17 4
 
< 0.1%
35118.1 4
 
< 0.1%
39284.17 4
 
< 0.1%
132848.88 4
 
< 0.1%
Other values (6783) 9224
92.2%
(Missing) 733
 
7.3%
ValueCountFrequency (%)
7006.035 3
< 0.1%
7006.52 1
 
< 0.1%
7021.91 2
< 0.1%
7039.745 2
< 0.1%
7056.405 1
 
< 0.1%
7059.455 1
 
< 0.1%
7079.32 2
< 0.1%
7080.7 1
 
< 0.1%
7084.365 1
 
< 0.1%
7085.39 1
 
< 0.1%
ValueCountFrequency (%)
24065688 1
< 0.1%
23917742 1
< 0.1%
23884555 1
< 0.1%
23784659 1
< 0.1%
23550326 1
< 0.1%
23070456 1
< 0.1%
22828106 1
< 0.1%
22641761 1
< 0.1%
22291103 1
< 0.1%
22255601 1
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6386
Distinct (%)75.1%
Missing1497
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean4138.8945
Minimum303.64542
Maximum15167.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:06.631794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum303.64542
5-th percentile844.6485
Q11616.4829
median3054.405
Q35817.1567
95-th percentile10710.132
Maximum15167.18
Range14863.535
Interquartile range (IQR)4200.6737

Descriptive statistics

Standard deviation3148.4266
Coefficient of variation (CV)0.76069264
Kurtosis0.61139451
Mean4138.8945
Median Absolute Deviation (MAD)1719.6008
Skewness1.1420884
Sum35193020
Variance9912589.9
MonotonicityNot monotonic
2023-03-14T16:53:06.806519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5758.89 5
 
0.1%
10895.32 4
 
< 0.1%
2933.606667 4
 
< 0.1%
6966.975 4
 
< 0.1%
3313.225 4
 
< 0.1%
11429.17 4
 
< 0.1%
1380.678333 4
 
< 0.1%
2368.514167 4
 
< 0.1%
7008.213333 4
 
< 0.1%
2497.22 4
 
< 0.1%
Other values (6376) 8462
84.6%
(Missing) 1497
 
15.0%
ValueCountFrequency (%)
303.6454167 2
< 0.1%
333.5966667 3
< 0.1%
355.2083333 1
 
< 0.1%
358.0583333 1
 
< 0.1%
378.9933333 1
 
< 0.1%
379.3908333 1
 
< 0.1%
382.7016667 1
 
< 0.1%
395.6641667 1
 
< 0.1%
396.3441667 1
 
< 0.1%
403.2541667 1
 
< 0.1%
ValueCountFrequency (%)
15167.18 1
< 0.1%
15115.19 1
< 0.1%
14978.33667 1
< 0.1%
14929.54 1
< 0.1%
14867.81333 1
< 0.1%
14856.48333 1
< 0.1%
14855.93 1
< 0.1%
14855.55667 1
< 0.1%
14839.7 1
< 0.1%
14836.73667 1
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4365
Minimum-1
Maximum1798
Zeros448
Zeros (%)4.5%
Negative1
Negative (%)< 0.1%
Memory size156.2 KiB
2023-03-14T16:53:07.017345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q14
median6
Q37
95-th percentile10
Maximum1798
Range1799
Interquartile range (IQR)3

Descriptive statistics

Standard deviation125.26956
Coefficient of variation (CV)6.7946499
Kurtosis117.52151
Mean18.4365
Median Absolute Deviation (MAD)2
Skewness10.598081
Sum184365
Variance15692.464
MonotonicityNot monotonic
2023-03-14T16:53:07.182825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1327
13.3%
8 1289
12.9%
7 1288
12.9%
5 1233
12.3%
4 1198
12.0%
3 1153
11.5%
9 539
5.4%
10 499
 
5.0%
1 472
 
4.7%
0 448
 
4.5%
Other values (134) 554
5.5%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 448
 
4.5%
1 472
 
4.7%
2 411
 
4.1%
3 1153
11.5%
4 1198
12.0%
5 1233
12.3%
6 1327
13.3%
7 1288
12.9%
8 1289
12.9%
ValueCountFrequency (%)
1798 1
< 0.1%
1783 1
< 0.1%
1760 1
< 0.1%
1756 1
< 0.1%
1733 1
< 0.1%
1724 1
< 0.1%
1668 1
< 0.1%
1665 1
< 0.1%
1662 1
< 0.1%
1644 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct201
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9372
Minimum0
Maximum1493
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:07.437737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q37
95-th percentile10
Maximum1493
Range1493
Interquartile range (IQR)3

Descriptive statistics

Standard deviation122.77268
Coefficient of variation (CV)5.8638536
Kurtosis83.931635
Mean20.9372
Median Absolute Deviation (MAD)1
Skewness8.9568974
Sum209372
Variance15073.13
MonotonicityNot monotonic
2023-03-14T16:53:07.642248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1926
19.3%
6 1676
16.8%
7 1662
16.6%
3 1321
13.2%
4 1316
13.2%
8 512
 
5.1%
9 481
 
4.8%
10 459
 
4.6%
1 222
 
2.2%
2 212
 
2.1%
Other values (191) 213
 
2.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 222
 
2.2%
2 212
 
2.1%
3 1321
13.2%
4 1316
13.2%
5 1926
19.3%
6 1676
16.8%
7 1662
16.6%
8 512
 
5.1%
9 481
 
4.8%
ValueCountFrequency (%)
1493 1
< 0.1%
1486 1
< 0.1%
1472 1
< 0.1%
1470 1
< 0.1%
1463 1
< 0.1%
1461 1
< 0.1%
1458 1
< 0.1%
1457 1
< 0.1%
1428 2
< 0.1%
1426 1
< 0.1%

Interest_Rate
Real number (ℝ)

Distinct217
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.2844
Minimum1
Maximum5789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:07.854244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median14
Q320
95-th percentile32
Maximum5789
Range5788
Interquartile range (IQR)12

Descriptive statistics

Standard deviation426.4454
Coefficient of variation (CV)6.5321179
Kurtosis97.404255
Mean65.2844
Median Absolute Deviation (MAD)6
Skewness9.5727674
Sum652844
Variance181855.68
MonotonicityNot monotonic
2023-03-14T16:53:08.014543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 514
 
5.1%
5 511
 
5.1%
10 457
 
4.6%
9 456
 
4.6%
6 455
 
4.5%
11 451
 
4.5%
7 448
 
4.5%
12 446
 
4.5%
18 424
 
4.2%
20 409
 
4.1%
Other values (207) 5429
54.3%
ValueCountFrequency (%)
1 283
2.8%
2 235
2.4%
3 249
2.5%
4 269
2.7%
5 511
5.1%
6 455
4.5%
7 448
4.5%
8 514
5.1%
9 456
4.6%
10 457
4.6%
ValueCountFrequency (%)
5789 1
< 0.1%
5771 1
< 0.1%
5721 1
< 0.1%
5694 1
< 0.1%
5618 1
< 0.1%
5598 1
< 0.1%
5544 1
< 0.1%
5447 1
< 0.1%
5423 1
< 0.1%
5419 1
< 0.1%

Num_of_Loan
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct48
Distinct (%)0.5%
Missing514
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean2.3762387
Minimum-100
Maximum1470
Zeros1023
Zeros (%)10.2%
Negative385
Negative (%)3.9%
Memory size156.2 KiB
2023-03-14T16:53:08.186528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum1470
Range1570
Interquartile range (IQR)4

Descriptive statistics

Standard deviation59.648411
Coefficient of variation (CV)25.102029
Kurtosis328.57574
Mean2.3762387
Median Absolute Deviation (MAD)2
Skewness16.424498
Sum22541
Variance3557.933
MonotonicityNot monotonic
2023-03-14T16:53:08.329371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 1440
14.4%
2 1410
14.1%
4 1394
13.9%
1 1030
10.3%
0 1023
10.2%
7 714
7.1%
6 708
7.1%
5 705
7.0%
-100 385
 
3.9%
9 340
 
3.4%
Other values (38) 337
 
3.4%
(Missing) 514
 
5.1%
ValueCountFrequency (%)
-100 385
 
3.9%
0 1023
10.2%
1 1030
10.3%
2 1410
14.1%
3 1440
14.4%
4 1394
13.9%
5 705
7.0%
6 708
7.1%
7 714
7.1%
8 300
 
3.0%
ValueCountFrequency (%)
1470 1
< 0.1%
1451 1
< 0.1%
1298 1
< 0.1%
1294 1
< 0.1%
1271 1
< 0.1%
1241 1
< 0.1%
1236 1
< 0.1%
1228 1
< 0.1%
1209 1
< 0.1%
1196 1
< 0.1%

Type_of_Loan
Categorical

HIGH CARDINALITY  MISSING 

Distinct3865
Distinct (%)43.6%
Missing1129
Missing (%)11.3%
Memory size1.2 MiB
Not Specified
 
164
Debt Consolidation Loan
 
149
Mortgage Loan
 
129
Credit-Builder Loan
 
121
Personal Loan
 
121
Other values (3860)
8187 

Length

Max length178
Median length138
Mean length66.555067
Min length9

Characters and Unicode

Total characters590410
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2233 ?
Unique (%)25.2%

Sample

1st rowNot Specified, Credit-Builder Loan, Home Equity Loan, Not Specified, Not Specified, Not Specified, and Mortgage Loan
2nd rowNot Specified, and Auto Loan
3rd rowMortgage Loan, Home Equity Loan, Payday Loan, Not Specified, and Student Loan
4th rowStudent Loan, Auto Loan, Auto Loan, Mortgage Loan, Auto Loan, Home Equity Loan, and Not Specified
5th rowCredit-Builder Loan, Payday Loan, and Student Loan

Common Values

ValueCountFrequency (%)
Not Specified 164
 
1.6%
Debt Consolidation Loan 149
 
1.5%
Mortgage Loan 129
 
1.3%
Credit-Builder Loan 121
 
1.2%
Personal Loan 121
 
1.2%
Auto Loan 121
 
1.2%
Student Loan 120
 
1.2%
Payday Loan 111
 
1.1%
Home Equity Loan 106
 
1.1%
Credit-Builder Loan, and Debt Consolidation Loan 33
 
0.3%
Other values (3855) 7696
77.0%
(Missing) 1129
 
11.3%

Length

2023-03-14T16:53:08.525453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan 31338
36.4%
and 7729
 
9.0%
credit-builder 4135
 
4.8%
payday 4020
 
4.7%
not 3979
 
4.6%
specified 3979
 
4.6%
personal 3910
 
4.5%
mortgage 3905
 
4.5%
student 3871
 
4.5%
home 3852
 
4.5%
Other values (4) 15326
17.8%

Most occurring characters

ValueCountFrequency (%)
77173
13.1%
o 62287
10.5%
a 58751
 
10.0%
n 54506
 
9.2%
e 35595
 
6.0%
t 35087
 
5.9%
d 31698
 
5.4%
L 31338
 
5.3%
i 27738
 
4.7%
, 26446
 
4.5%
Other values (23) 149791
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400206
67.8%
Uppercase Letter 82450
 
14.0%
Space Separator 77173
 
13.1%
Other Punctuation 26446
 
4.5%
Dash Punctuation 4135
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 62287
15.6%
a 58751
14.7%
n 54506
13.6%
e 35595
8.9%
t 35087
8.8%
d 31698
7.9%
i 27738
6.9%
r 16085
 
4.0%
u 15674
 
3.9%
y 11892
 
3.0%
Other values (9) 50893
12.7%
Uppercase Letter
ValueCountFrequency (%)
L 31338
38.0%
C 7964
 
9.7%
P 7930
 
9.6%
S 7850
 
9.5%
B 4135
 
5.0%
N 3979
 
4.8%
M 3905
 
4.7%
H 3852
 
4.7%
E 3852
 
4.7%
D 3829
 
4.6%
Space Separator
ValueCountFrequency (%)
77173
100.0%
Other Punctuation
ValueCountFrequency (%)
, 26446
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 482656
81.7%
Common 107754
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 62287
12.9%
a 58751
12.2%
n 54506
11.3%
e 35595
 
7.4%
t 35087
 
7.3%
d 31698
 
6.6%
L 31338
 
6.5%
i 27738
 
5.7%
r 16085
 
3.3%
u 15674
 
3.2%
Other values (20) 113897
23.6%
Common
ValueCountFrequency (%)
77173
71.6%
, 26446
 
24.5%
- 4135
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77173
13.1%
o 62287
10.5%
a 58751
 
10.0%
n 54506
 
9.2%
e 35595
 
6.0%
t 35087
 
5.9%
d 31698
 
5.4%
L 31338
 
5.3%
i 27738
 
4.7%
, 26446
 
4.5%
Other values (23) 149791
25.4%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0334
Minimum-5
Maximum67
Zeros108
Zeros (%)1.1%
Negative70
Negative (%)0.7%
Memory size156.2 KiB
2023-03-14T16:53:08.749218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile53
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.846519
Coefficient of variation (CV)0.70585445
Kurtosis0.29834213
Mean21.0334
Median Absolute Deviation (MAD)9
Skewness0.94417818
Sum210334
Variance220.41913
MonotonicityNot monotonic
2023-03-14T16:53:08.961774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 354
 
3.5%
14 342
 
3.4%
15 341
 
3.4%
6 341
 
3.4%
13 340
 
3.4%
9 321
 
3.2%
8 319
 
3.2%
5 312
 
3.1%
12 309
 
3.1%
10 302
 
3.0%
Other values (63) 6719
67.2%
ValueCountFrequency (%)
-5 3
 
< 0.1%
-4 8
 
0.1%
-3 13
 
0.1%
-2 22
 
0.2%
-1 24
 
0.2%
0 108
1.1%
1 147
1.5%
2 136
1.4%
3 170
1.7%
4 165
1.7%
ValueCountFrequency (%)
67 1
 
< 0.1%
66 2
 
< 0.1%
65 6
 
0.1%
64 6
 
0.1%
63 6
 
0.1%
62 50
0.5%
61 39
0.4%
60 47
0.5%
59 62
0.6%
58 59
0.6%

Num_of_Delayed_Payment
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct105
Distinct (%)1.2%
Missing972
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean32.806602
Minimum-3
Maximum4397
Zeros146
Zeros (%)1.5%
Negative55
Negative (%)0.5%
Memory size156.2 KiB
2023-03-14T16:53:09.142759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile2
Q19
median14
Q318
95-th percentile24
Maximum4397
Range4400
Interquartile range (IQR)9

Descriptive statistics

Standard deviation240.84067
Coefficient of variation (CV)7.3412256
Kurtosis199.00663
Mean32.806602
Median Absolute Deviation (MAD)5
Skewness13.741653
Sum296178
Variance58004.226
MonotonicityNot monotonic
2023-03-14T16:53:09.283600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 559
 
5.6%
10 534
 
5.3%
17 533
 
5.3%
18 530
 
5.3%
16 510
 
5.1%
20 508
 
5.1%
15 489
 
4.9%
8 463
 
4.6%
9 463
 
4.6%
11 459
 
4.6%
Other values (95) 3980
39.8%
(Missing) 972
 
9.7%
ValueCountFrequency (%)
-3 8
 
0.1%
-2 25
 
0.2%
-1 22
 
0.2%
0 146
1.5%
1 149
1.5%
2 185
1.8%
3 202
2.0%
4 170
1.7%
5 195
1.9%
6 231
2.3%
ValueCountFrequency (%)
4397 1
< 0.1%
4337 1
< 0.1%
4239 1
< 0.1%
4164 1
< 0.1%
4077 1
< 0.1%
4051 1
< 0.1%
4043 1
< 0.1%
4042 1
< 0.1%
4019 1
< 0.1%
3960 1
< 0.1%

Changed_Credit_Limit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2704
Distinct (%)27.6%
Missing220
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean10.457561
Minimum-5.95
Maximum34.02
Zeros0
Zeros (%)0.0%
Negative150
Negative (%)1.5%
Memory size156.2 KiB
2023-03-14T16:53:09.429415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5.95
5-th percentile1.22
Q15.36
median9.41
Q314.9525
95-th percentile23.68
Maximum34.02
Range39.97
Interquartile range (IQR)9.5925

Descriptive statistics

Standard deviation6.8086069
Coefficient of variation (CV)0.65107024
Kurtosis0.054590647
Mean10.457561
Median Absolute Deviation (MAD)4.59
Skewness0.63734366
Sum102274.95
Variance46.357129
MonotonicityNot monotonic
2023-03-14T16:53:09.582742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.23 19
 
0.2%
7.35 18
 
0.2%
8.3 16
 
0.2%
11.8 14
 
0.1%
11.5 14
 
0.1%
8.99 14
 
0.1%
3.06 14
 
0.1%
10.39 14
 
0.1%
11.63 14
 
0.1%
8.74 14
 
0.1%
Other values (2694) 9629
96.3%
(Missing) 220
 
2.2%
ValueCountFrequency (%)
-5.95 1
< 0.1%
-5.8 1
< 0.1%
-5.78 1
< 0.1%
-5.58 1
< 0.1%
-5.5 1
< 0.1%
-5.48 1
< 0.1%
-5.38 1
< 0.1%
-5.29 1
< 0.1%
-5.17 1
< 0.1%
-5.11 1
< 0.1%
ValueCountFrequency (%)
34.02 1
< 0.1%
33.74 1
< 0.1%
33.43 1
< 0.1%
33.26 1
< 0.1%
33.02 1
< 0.1%
32.82 1
< 0.1%
32.71 1
< 0.1%
32.47 1
< 0.1%
32.44 1
< 0.1%
32.22 1
< 0.1%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct178
Distinct (%)1.8%
Missing215
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean29.180174
Minimum0
Maximum2589
Zeros707
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:09.739263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile13
Maximum2589
Range2589
Interquartile range (IQR)6

Descriptive statistics

Standard deviation201.72188
Coefficient of variation (CV)6.9129772
Kurtosis96.30073
Mean29.180174
Median Absolute Deviation (MAD)3
Skewness9.5766572
Sum285528
Variance40691.715
MonotonicityNot monotonic
2023-03-14T16:53:10.192672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1110
11.1%
3 846
8.5%
2 844
8.4%
6 841
8.4%
7 822
8.2%
8 804
 
8.0%
1 729
 
7.3%
0 707
 
7.1%
5 550
 
5.5%
9 521
 
5.2%
Other values (168) 2011
20.1%
ValueCountFrequency (%)
0 707
7.1%
1 729
7.3%
2 844
8.4%
3 846
8.5%
4 1110
11.1%
5 550
5.5%
6 841
8.4%
7 822
8.2%
8 804
8.0%
9 521
5.2%
ValueCountFrequency (%)
2589 1
< 0.1%
2572 1
< 0.1%
2565 1
< 0.1%
2564 1
< 0.1%
2544 1
< 0.1%
2542 1
< 0.1%
2540 1
< 0.1%
2531 1
< 0.1%
2514 1
< 0.1%
2495 1
< 0.1%

Credit_Mix
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2025
Missing (%)20.2%
Memory size624.5 KiB
1.0
3613 
2.0
2431 
0.0
1931 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23925
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 3613
36.1%
2.0 2431
24.3%
0.0 1931
19.3%
(Missing) 2025
20.2%

Length

2023-03-14T16:53:10.326908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:53:10.445553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3613
45.3%
2.0 2431
30.5%
0.0 1931
24.2%

Most occurring characters

ValueCountFrequency (%)
0 9906
41.4%
. 7975
33.3%
1 3613
 
15.1%
2 2431
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15950
66.7%
Other Punctuation 7975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9906
62.1%
1 3613
 
22.7%
2 2431
 
15.2%
Other Punctuation
ValueCountFrequency (%)
. 7975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9906
41.4%
. 7975
33.3%
1 3613
 
15.1%
2 2431
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9906
41.4%
. 7975
33.3%
1 3613
 
15.1%
2 2431
 
10.2%

Outstanding_Debt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6954
Distinct (%)70.3%
Missing108
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1438.4831
Minimum0.23
Maximum4997.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:10.565454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile120.7815
Q1569.9125
median1172.375
Q31955.4425
95-th percentile4146.12
Maximum4997.05
Range4996.82
Interquartile range (IQR)1385.53

Descriptive statistics

Standard deviation1165.3385
Coefficient of variation (CV)0.81011617
Kurtosis0.8883136
Mean1438.4831
Median Absolute Deviation (MAD)641.585
Skewness1.2098534
Sum14229475
Variance1358013.7
MonotonicityNot monotonic
2023-03-14T16:53:10.701678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.65 6
 
0.1%
90.14 5
 
0.1%
952.39 5
 
0.1%
1290.92 5
 
0.1%
242.96 5
 
0.1%
1478.07 5
 
0.1%
4679.1 5
 
0.1%
2984.04 4
 
< 0.1%
1747.16 4
 
< 0.1%
1109.03 4
 
< 0.1%
Other values (6944) 9844
98.4%
(Missing) 108
 
1.1%
ValueCountFrequency (%)
0.23 2
< 0.1%
0.54 2
< 0.1%
0.95 1
 
< 0.1%
1.2 3
< 0.1%
1.23 1
 
< 0.1%
1.3 2
< 0.1%
1.37 1
 
< 0.1%
1.48 1
 
< 0.1%
2.13 2
< 0.1%
2.43 1
 
< 0.1%
ValueCountFrequency (%)
4997.05 2
< 0.1%
4990.91 1
< 0.1%
4987.19 1
< 0.1%
4986.03 1
< 0.1%
4984.82 1
< 0.1%
4977.18 2
< 0.1%
4975.63 2
< 0.1%
4974.81 1
< 0.1%
4974.31 2
< 0.1%
4973.13 2
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.28398
Minimum21.02869
Maximum49.522324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:10.836645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21.02869
5-th percentile24.205713
Q128.011381
median32.287898
Q336.545094
95-th percentile40.171244
Maximum49.522324
Range28.493634
Interquartile range (IQR)8.5337123

Descriptive statistics

Standard deviation5.1452225
Coefficient of variation (CV)0.15937386
Kurtosis-0.94701264
Mean32.28398
Median Absolute Deviation (MAD)4.2671692
Skewness0.019780504
Sum322839.8
Variance26.473315
MonotonicityNot monotonic
2023-03-14T16:53:10.966638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.64514844 1
 
< 0.1%
27.2694593 1
 
< 0.1%
25.1728871 1
 
< 0.1%
34.79109016 1
 
< 0.1%
35.82704849 1
 
< 0.1%
34.71654926 1
 
< 0.1%
35.17808152 1
 
< 0.1%
27.32894603 1
 
< 0.1%
40.37206297 1
 
< 0.1%
32.80172553 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
21.02869026 1
< 0.1%
21.0567212 1
< 0.1%
21.28084538 1
< 0.1%
21.48488992 1
< 0.1%
21.51493181 1
< 0.1%
21.62851115 1
< 0.1%
21.63021388 1
< 0.1%
21.64355025 1
< 0.1%
21.66371928 1
< 0.1%
21.66666958 1
< 0.1%
ValueCountFrequency (%)
49.5223243 1
< 0.1%
48.33729091 1
< 0.1%
48.1765989 1
< 0.1%
47.93798002 1
< 0.1%
47.5559826 1
< 0.1%
47.17844594 1
< 0.1%
46.51063306 1
< 0.1%
46.2937028 1
< 0.1%
45.94099045 1
< 0.1%
45.80611561 1
< 0.1%

Credit_History_Age
Categorical

HIGH CARDINALITY  MISSING 

Distinct401
Distinct (%)4.4%
Missing880
Missing (%)8.8%
Memory size800.2 KiB
15 Years and 11 Months
 
51
17 Years and 9 Months
 
50
16 Years and 5 Months
 
49
19 Years and 5 Months
 
48
17 Years and 2 Months
 
47
Other values (396)
8875 

Length

Max length22
Median length21
Mean length20.988268
Min length20

Characters and Unicode

Total characters191413
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28 Years and 3 Months
2nd row17 Years and 7 Months
3rd row19 Years and 5 Months
4th row13 Years and 5 Months
5th row16 Years and 5 Months

Common Values

ValueCountFrequency (%)
15 Years and 11 Months 51
 
0.5%
17 Years and 9 Months 50
 
0.5%
16 Years and 5 Months 49
 
0.5%
19 Years and 5 Months 48
 
0.5%
17 Years and 2 Months 47
 
0.5%
18 Years and 9 Months 46
 
0.5%
19 Years and 3 Months 46
 
0.5%
17 Years and 8 Months 45
 
0.4%
18 Years and 3 Months 45
 
0.4%
15 Years and 8 Months 45
 
0.4%
Other values (391) 8648
86.5%
(Missing) 880
 
8.8%

Length

2023-03-14T16:53:11.123748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 9120
20.0%
and 9120
20.0%
months 9120
20.0%
11 1090
 
2.4%
9 1059
 
2.3%
8 1054
 
2.3%
10 1053
 
2.3%
5 999
 
2.2%
6 959
 
2.1%
7 945
 
2.1%
Other values (27) 11081
24.3%

Most occurring characters

ValueCountFrequency (%)
36480
19.1%
a 18240
9.5%
s 18240
9.5%
n 18240
9.5%
o 9120
 
4.8%
t 9120
 
4.8%
Y 9120
 
4.8%
e 9120
 
4.8%
r 9120
 
4.8%
d 9120
 
4.8%
Other values (12) 45493
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 109440
57.2%
Space Separator 36480
 
19.1%
Decimal Number 27253
 
14.2%
Uppercase Letter 18240
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7702
28.3%
2 4580
16.8%
3 2476
 
9.1%
0 2430
 
8.9%
9 1802
 
6.6%
8 1800
 
6.6%
7 1705
 
6.3%
6 1686
 
6.2%
5 1646
 
6.0%
4 1426
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
a 18240
16.7%
s 18240
16.7%
n 18240
16.7%
o 9120
8.3%
t 9120
8.3%
e 9120
8.3%
r 9120
8.3%
d 9120
8.3%
h 9120
8.3%
Uppercase Letter
ValueCountFrequency (%)
Y 9120
50.0%
M 9120
50.0%
Space Separator
ValueCountFrequency (%)
36480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127680
66.7%
Common 63733
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
36480
57.2%
1 7702
 
12.1%
2 4580
 
7.2%
3 2476
 
3.9%
0 2430
 
3.8%
9 1802
 
2.8%
8 1800
 
2.8%
7 1705
 
2.7%
6 1686
 
2.6%
5 1646
 
2.6%
Latin
ValueCountFrequency (%)
a 18240
14.3%
s 18240
14.3%
n 18240
14.3%
o 9120
7.1%
t 9120
7.1%
Y 9120
7.1%
e 9120
7.1%
r 9120
7.1%
d 9120
7.1%
M 9120
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36480
19.1%
a 18240
9.5%
s 18240
9.5%
n 18240
9.5%
o 9120
 
4.8%
t 9120
 
4.8%
Y 9120
 
4.8%
e 9120
 
4.8%
r 9120
 
4.8%
d 9120
 
4.8%
Other values (12) 45493
23.8%

Payment_of_Min_Amount
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1163
Missing (%)11.6%
Memory size641.3 KiB
1.0
5305 
0.0
3532 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26511
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 5305
53.0%
0.0 3532
35.3%
(Missing) 1163
 
11.6%

Length

2023-03-14T16:53:11.248758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:53:11.359175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5305
60.0%
0.0 3532
40.0%

Most occurring characters

ValueCountFrequency (%)
0 12369
46.7%
. 8837
33.3%
1 5305
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17674
66.7%
Other Punctuation 8837
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12369
70.0%
1 5305
30.0%
Other Punctuation
ValueCountFrequency (%)
. 8837
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26511
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12369
46.7%
. 8837
33.3%
1 5305
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26511
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12369
46.7%
. 8837
33.3%
1 5305
20.0%

Total_EMI_per_month
Real number (ℝ)

Distinct6573
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1358.6642
Minimum0
Maximum82236
Zeros1056
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:11.474893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.540062
median69.03215
Q3154.86149
95-th percentile429.52657
Maximum82236
Range82236
Interquartile range (IQR)125.32142

Descriptive statistics

Standard deviation8112.2101
Coefficient of variation (CV)5.9707248
Kurtosis54.388324
Mean1358.6642
Median Absolute Deviation (MAD)49.302251
Skewness7.2327096
Sum13586642
Variance65807952
MonotonicityNot monotonic
2023-03-14T16:53:11.597318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1056
 
10.6%
103.3976742 5
 
0.1%
133.9027918 5
 
0.1%
114.2482426 5
 
0.1%
79.71165235 4
 
< 0.1%
45.18912916 4
 
< 0.1%
13.45356846 4
 
< 0.1%
150.5977429 4
 
< 0.1%
16.50138403 4
 
< 0.1%
993.4409317 4
 
< 0.1%
Other values (6563) 8905
89.0%
ValueCountFrequency (%)
0 1056
10.6%
4.462837467 1
 
< 0.1%
4.916138542 1
 
< 0.1%
5.138484696 1
 
< 0.1%
5.218466359 1
 
< 0.1%
5.262291048 1
 
< 0.1%
5.629824417 2
 
< 0.1%
5.76627588 1
 
< 0.1%
5.905518076 1
 
< 0.1%
5.968634609 2
 
< 0.1%
ValueCountFrequency (%)
82236 1
< 0.1%
82178 1
< 0.1%
80983 1
< 0.1%
80975 1
< 0.1%
80850 1
< 0.1%
80832 1
< 0.1%
80308 1
< 0.1%
79849 1
< 0.1%
79722 1
< 0.1%
78605 1
< 0.1%

Amount_invested_monthly
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9076
Distinct (%)99.9%
Missing912
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean195.08605
Minimum0
Maximum1785.7868
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:11.725468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.053878
Q172.814899
median129.1154
Q3237.237
95-th percentile607.6071
Maximum1785.7868
Range1785.7868
Interquartile range (IQR)164.4221

Descriptive statistics

Standard deviation197.49683
Coefficient of variation (CV)1.0123575
Kurtosis8.3019475
Mean195.08605
Median Absolute Deviation (MAD)68.799801
Skewness2.5311971
Sum1772942
Variance39004.996
MonotonicityNot monotonic
2023-03-14T16:53:11.855606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.1%
130.4483236 1
 
< 0.1%
128.0247733 1
 
< 0.1%
162.8164817 1
 
< 0.1%
253.1441732 1
 
< 0.1%
56.6599196 1
 
< 0.1%
21.79321222 1
 
< 0.1%
87.58741095 1
 
< 0.1%
96.0148337 1
 
< 0.1%
265.3030171 1
 
< 0.1%
Other values (9066) 9066
90.7%
(Missing) 912
 
9.1%
ValueCountFrequency (%)
0 13
0.1%
10.13191094 1
 
< 0.1%
10.2494613 1
 
< 0.1%
10.48334941 1
 
< 0.1%
10.50805276 1
 
< 0.1%
10.52769613 1
 
< 0.1%
10.57504709 1
 
< 0.1%
10.70103155 1
 
< 0.1%
10.74448184 1
 
< 0.1%
10.76646972 1
 
< 0.1%
ValueCountFrequency (%)
1785.786788 1
< 0.1%
1637.743148 1
< 0.1%
1611.277939 1
< 0.1%
1495.597779 1
< 0.1%
1461.722698 1
< 0.1%
1459.649193 1
< 0.1%
1421.894603 1
< 0.1%
1413.39639 1
< 0.1%
1393.686271 1
< 0.1%
1386.171937 1
< 0.1%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size916.7 KiB
Low_spent_Small_value_payments
2537 
High_spent_Medium_value_payments
1765 
Low_spent_Medium_value_payments
1379 
High_spent_Large_value_payments
1344 
Low_spent_Large_value_payments
1099 
Other values (2)
1876 

Length

Max length32
Median length31
Mean length28.8704
Min length6

Characters and Unicode

Total characters288704
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_spent_Medium_value_payments
2nd rowLow_spent_Small_value_payments
3rd rowLow_spent_Small_value_payments
4th rowHigh_spent_Large_value_payments
5th rowLow_spent_Large_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 2537
25.4%
High_spent_Medium_value_payments 1765
17.6%
Low_spent_Medium_value_payments 1379
13.8%
High_spent_Large_value_payments 1344
13.4%
Low_spent_Large_value_payments 1099
11.0%
High_spent_Small_value_payments 1099
11.0%
!@9#%8 777
 
7.8%

Length

2023-03-14T16:53:11.987069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:53:12.125158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 2537
25.4%
high_spent_medium_value_payments 1765
17.6%
low_spent_medium_value_payments 1379
13.8%
high_spent_large_value_payments 1344
13.4%
low_spent_large_value_payments 1099
11.0%
high_spent_small_value_payments 1099
11.0%
9#%8 777
 
7.8%

Most occurring characters

ValueCountFrequency (%)
_ 36892
12.8%
e 33256
11.5%
a 24525
 
8.5%
s 18446
 
6.4%
p 18446
 
6.4%
n 18446
 
6.4%
t 18446
 
6.4%
l 16495
 
5.7%
m 16003
 
5.5%
u 12367
 
4.3%
Other values (19) 75382
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228704
79.2%
Connector Punctuation 36892
 
12.8%
Uppercase Letter 18446
 
6.4%
Other Punctuation 3108
 
1.1%
Decimal Number 1554
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33256
14.5%
a 24525
10.7%
s 18446
8.1%
p 18446
8.1%
n 18446
8.1%
t 18446
8.1%
l 16495
 
7.2%
m 16003
 
7.0%
u 12367
 
5.4%
y 9223
 
4.0%
Other values (8) 43051
18.8%
Uppercase Letter
ValueCountFrequency (%)
L 7458
40.4%
H 4208
22.8%
S 3636
19.7%
M 3144
17.0%
Other Punctuation
ValueCountFrequency (%)
! 777
25.0%
@ 777
25.0%
# 777
25.0%
% 777
25.0%
Decimal Number
ValueCountFrequency (%)
9 777
50.0%
8 777
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 36892
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 247150
85.6%
Common 41554
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33256
13.5%
a 24525
 
9.9%
s 18446
 
7.5%
p 18446
 
7.5%
n 18446
 
7.5%
t 18446
 
7.5%
l 16495
 
6.7%
m 16003
 
6.5%
u 12367
 
5.0%
y 9223
 
3.7%
Other values (12) 61497
24.9%
Common
ValueCountFrequency (%)
_ 36892
88.8%
! 777
 
1.9%
@ 777
 
1.9%
9 777
 
1.9%
# 777
 
1.9%
% 777
 
1.9%
8 777
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 36892
12.8%
e 33256
11.5%
a 24525
 
8.5%
s 18446
 
6.4%
p 18446
 
6.4%
n 18446
 
6.4%
t 18446
 
6.4%
l 16495
 
5.7%
m 16003
 
5.5%
u 12367
 
4.3%
Other values (19) 75382
26.1%

Monthly_Balance
Real number (ℝ)

Distinct9900
Distinct (%)100.0%
Missing100
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean399.51284
Minimum0.0077596648
Maximum1566.6132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:53:12.301535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0077596648
5-th percentile172.61634
Q1268.22749
median333.9406
Q3466.13006
95-th percentile866.17058
Maximum1566.6132
Range1566.6054
Interquartile range (IQR)197.90257

Descriptive statistics

Standard deviation213.88996
Coefficient of variation (CV)0.53537694
Kurtosis2.9828962
Mean399.51284
Median Absolute Deviation (MAD)83.531478
Skewness1.621532
Sum3955177.2
Variance45748.916
MonotonicityNot monotonic
2023-03-14T16:53:12.428066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
428.4718511 1
 
< 0.1%
429.2036169 1
 
< 0.1%
272.0218675 1
 
< 0.1%
336.4321874 1
 
< 0.1%
1112.153419 1
 
< 0.1%
356.2725868 1
 
< 0.1%
191.2277487 1
 
< 0.1%
814.8886216 1
 
< 0.1%
340.3650251 1
 
< 0.1%
375.5233445 1
 
< 0.1%
Other values (9890) 9890
98.9%
(Missing) 100
 
1.0%
ValueCountFrequency (%)
0.007759664775 1
< 0.1%
0.4191236108 1
< 0.1%
1.77998453 1
< 0.1%
1.987138164 1
< 0.1%
3.512807587 1
< 0.1%
4.158195232 1
< 0.1%
4.388830932 1
< 0.1%
4.469998434 1
< 0.1%
5.175799256 1
< 0.1%
6.464010233 1
< 0.1%
ValueCountFrequency (%)
1566.613165 1
< 0.1%
1518.393447 1
< 0.1%
1516.080352 1
< 0.1%
1474.356118 1
< 0.1%
1460.917186 1
< 0.1%
1429.681432 1
< 0.1%
1409.858767 1
< 0.1%
1388.993782 1
< 0.1%
1373.111241 1
< 0.1%
1369.559421 1
< 0.1%

Credit_Score
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size644.5 KiB
1
5350 
0
2898 
2
1752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Length

2023-03-14T16:53:12.587775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:53:12.703954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Most occurring characters

ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5350
53.5%
0 2898
29.0%
2 1752
 
17.5%

Interactions

2023-03-14T16:53:00.194147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:06.869015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-03-14T16:52:27.962631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:31.430143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:34.783701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:38.218117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.179145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:43.798373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:46.469625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:48.929665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:51.576893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:54.647716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:58.596963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:53:02.316556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:08.982271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:12.275633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:17.058919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:20.708459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:24.775843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:28.110842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:31.646467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:35.184611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:38.364985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.318310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:43.956486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:46.605492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:49.090632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:51.725046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:54.916357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:58.889778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:53:02.518876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:09.178813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:12.439563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:17.291922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:20.933939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:24.959130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:28.328078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:31.814831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:35.388878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:38.601796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.459780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:44.115940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:46.735956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:49.251757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:51.866081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:55.112222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:59.133759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:53:02.709913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:09.365495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:12.609722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:17.525518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:21.188657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:25.144966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:28.555515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:32.002612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:35.569459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:38.838375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.599845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:44.284451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:46.873192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:49.412673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:52.011915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:55.305489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:59.404698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:53:02.889178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:09.544530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:12.836578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:17.718972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:21.526134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:25.317969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:28.742133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:32.154620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:35.729853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:39.021319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.729034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:44.433559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:46.995098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:49.556520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:52.179846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:55.467533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:59.628780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:53:03.083318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:09.734643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:13.066380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:17.951603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:21.815015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:25.734510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:28.958576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:32.361784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:35.921987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:39.238651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:41.869581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:44.601532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:47.147612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:49.707993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:52.371542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:55.641311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:52:59.941744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-03-14T16:53:12.835048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
MonthAgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceOccupationCredit_MixPayment_of_Min_AmountPayment_BehaviourCredit_Score
Month1.0000.000-0.0080.0020.0030.0040.006-0.002-0.004-0.0060.0020.1350.0040.0060.0220.018-0.0050.0000.0000.0000.0170.031
Age0.0001.0000.0570.062-0.157-0.114-0.178-0.178-0.148-0.151-0.128-0.219-0.1900.029-0.0920.0350.1050.0000.0000.0000.0170.000
Annual_Income-0.0080.0571.0000.979-0.268-0.193-0.269-0.232-0.226-0.255-0.157-0.247-0.2680.1290.4450.6230.5780.0150.0000.0050.0000.000
Monthly_Inhand_Salary0.0020.0620.9791.000-0.268-0.199-0.269-0.235-0.227-0.253-0.160-0.248-0.2710.1240.4470.6340.5900.0290.2860.3470.1730.172
Num_Bank_Accounts0.003-0.157-0.268-0.2681.0000.3980.5590.4020.5530.5590.2890.4980.498-0.0730.118-0.184-0.2910.0120.0000.0000.0000.015
Num_Credit_Card0.004-0.114-0.193-0.1990.3981.0000.4320.3310.4330.3880.1910.3970.450-0.0530.098-0.135-0.2240.0000.0000.0000.0190.000
Interest_Rate0.006-0.178-0.269-0.2690.5590.4321.0000.4650.5540.5380.3250.5790.587-0.0770.147-0.195-0.3110.0170.0000.0000.0000.003
Num_of_Loan-0.002-0.178-0.232-0.2350.4020.3310.4651.0000.4120.4130.2920.4980.514-0.0960.494-0.169-0.4360.0060.0000.0140.0000.000
Delay_from_due_date-0.004-0.148-0.226-0.2270.5530.4330.5540.4121.0000.5530.2700.5030.529-0.0850.142-0.161-0.2720.0260.5860.5410.0350.341
Num_of_Delayed_Payment-0.006-0.151-0.255-0.2530.5590.3880.5380.4130.5531.0000.2910.4780.480-0.0660.128-0.173-0.2890.0000.0000.0000.0000.000
Changed_Credit_Limit0.002-0.128-0.157-0.1600.2890.1910.3250.2920.2700.2911.0000.3630.320-0.0450.104-0.122-0.1830.0200.4410.5190.0280.166
Num_Credit_Inquiries0.135-0.219-0.247-0.2480.4980.3970.5790.4980.5030.4780.3631.0000.580-0.0770.208-0.175-0.3050.0080.0000.0000.0200.000
Outstanding_Debt0.004-0.190-0.268-0.2710.4980.4500.5870.5140.5290.4800.3200.5801.000-0.0780.178-0.189-0.3390.0310.5910.5670.0530.380
Credit_Utilization_Ratio0.0060.0290.1290.124-0.073-0.053-0.077-0.096-0.085-0.066-0.045-0.077-0.0781.000-0.002-0.0000.1920.0140.1010.1330.0750.051
Total_EMI_per_month0.022-0.0920.4450.4470.1180.0980.1470.4940.1420.1280.1040.2080.178-0.0021.0000.2740.0020.0170.0210.0050.0120.000
Amount_invested_monthly0.0180.0350.6230.634-0.184-0.135-0.195-0.169-0.161-0.173-0.122-0.175-0.189-0.0000.2741.000-0.0390.0040.1590.1990.1280.119
Monthly_Balance-0.0050.1050.5780.590-0.291-0.224-0.311-0.436-0.272-0.289-0.183-0.305-0.3390.1920.002-0.0391.0000.0120.2870.3320.2420.147
Occupation0.0000.0000.0150.0290.0120.0000.0170.0060.0260.0000.0200.0080.0310.0140.0170.0040.0121.0000.0520.0270.0170.024
Credit_Mix0.0000.0000.0000.2860.0000.0000.0000.0000.5860.0000.4410.0000.5910.1010.0210.1590.2870.0521.0000.8310.0860.448
Payment_of_Min_Amount0.0000.0000.0050.3470.0000.0000.0000.0140.5410.0000.5190.0000.5670.1330.0050.1990.3320.0270.8311.0000.1040.475
Payment_Behaviour0.0170.0170.0000.1730.0000.0190.0000.0000.0350.0000.0280.0200.0530.0750.0120.1280.2420.0170.0860.1041.0000.084
Credit_Score0.0310.0000.0000.1720.0150.0000.0030.0000.3410.0000.1660.0000.3800.0510.0000.1190.1470.0240.4480.4750.0841.000

Missing values

2023-03-14T16:53:03.451683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-14T16:53:04.309438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-14T16:53:05.007284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer_IDMonthAgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
87607CUS_0x4662825.0_______112282.4009102.8666674219160.0NaN2614.09.633.01.01113.9839.45000728 Years and 3 MonthsNaN0.000000560.034145Low_spent_Medium_value_payments630.2525222
81732CUS_0xb2a1522.0Developer14006.7451017.2287506116877.0Not Specified, Credit-Builder Loan, Home Equity Loan, Not Specified, Not Specified, Not Specified, and Mortgage Loan1711.011.064.01.01347.3138.38367617 Years and 7 MonthsNaN64.236679137.169856Low_spent_Small_value_payments190.3163401
75810CUS_0x363d322.0Architect20655.0701726.2558337592.0Not Specified, and Auto Loan1913.011.8810.01.01027.8335.00814219 Years and 5 Months1.027.516482201.231799Low_spent_Small_value_payments233.8773031
58923CUS_0xc1e7424.0Journalist38721.4403340.7866676639195.0Mortgage Loan, Home Equity Loan, Payday Loan, Not Specified, and Student Loan2420.020.878.00.03467.8233.42567713 Years and 5 Months1.087.26862843.234498High_spent_Large_value_payments443.5755401
88806CUS_0x26a9727.0Media_Manager15155.0101473.91750089217.0Student Loan, Auto Loan, Auto Loan, Mortgage Loan, Auto Loan, Home Equity Loan, and Not Specified2222.07.7011.00.02525.4237.17203016 Years and 5 Months1.062.89112097.309012Low_spent_Large_value_payments257.1916181
66675CUS_0x5044423.0LawyerNaN1604.17333355163.0Credit-Builder Loan, Payday Loan, and Student Loan2217.018.0210.01.01618.9427.8011056 Years and 10 Months1.034.19404333.328452High_spent_Medium_value_payments342.8948381
70872CUS_0x975d1NaNDoctor143883.28012131.2733334344.0Credit-Builder Loan, Debt Consolidation Loan, Credit-Builder Loan, and Auto Loan277.03.564.02.0354.4129.83348924 Years and 1 Months0.0398.496197265.907953!@9#%8808.7231840
5537CUS_0x867d248.0Scientist144468.72011788.0600003373.0Debt Consolidation Loan, Credit-Builder Loan, and Personal Loan-210.03.014.02.0539.7426.021170NaN0.0241.399048785.950518High_spent_Small_value_payments411.4564342
27629CUS_0x57c6-500.0_______NaNNaN87168.0Student Loan, Credit-Builder Loan, Personal Loan, Home Equity Loan, Auto Loan, Credit-Builder Loan, Personal Loan, and Credit-Builder Loan3523.029.8513.0NaN3528.8832.273659NaN1.0291.20914496.016104High_spent_Large_value_payments280.0125021
49445CUS_0x4056646.0_______71842.8006101.90000037122.0Payday Loan, and Credit-Builder Loan1310.07.414.0NaN39.3141.19738727 Years and 0 MonthsNaN65.27357270.035562High_spent_Medium_value_payments724.8808661
Customer_IDMonthAgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
17757CUS_0xeb6622.0_______16472.8701554.7391677532NaNStudent Loan, Student Loan, Auto Loan, Debt Consolidation Loan, Mortgage Loan, Payday Loan, Personal Loan, Debt Consolidation Loan, and Home Equity Loan2017.09.658.00.02140.9636.96744718 Years and 10 Months1.066.723994NaN!@9#%8307.8202720
31933CUS_0x2d1e627.0Engineer44590.1003605.8416675581.0Auto Loan69.02.512.02.0555.4832.91327421 Years and 9 Months0.042591.00000055.817135!@9#%8510.9698391
4653CUS_0x17a9639.0Musician83386.5207081.876667106237.0Student Loan, Auto Loan, Credit-Builder Loan, Credit-Builder Loan, Personal Loan, Payday Loan, and Payday Loan3622.022.308.00.04972.4032.1005546 Years and 1 Months1.0482.179783231.016806High_spent_Medium_value_payments244.9910781
42466CUS_0x6743340.0Journalist37840.640NaN6552.0Debt Consolidation Loan, and Mortgage Loan2416.018.546.01.02641.9135.77360415 Years and 0 Months1.060.68036023.710268High_spent_Large_value_payments446.9480381
66002CUS_0x924a318.0Journalist9829.665970.138750810187.0Not Specified, Home Equity Loan, Not Specified, Personal Loan, Auto Loan, Home Equity Loan, and Auto Loan5419.011.549.00.02783.2929.1113865 Years and 7 Months1.041.54009696.902056Low_spent_Medium_value_payments238.5717231
48908CUS_0x737a528.0_______142277.720NaN346NaNNaN137.02.183.0NaN324.6038.34481919 Years and 9 Months0.00.000000113.582217High_spent_Large_value_payments1326.8654502
34789CUS_0x1b6d622.0_______16753.6301564.13583369239.0Credit-Builder Loan, Student Loan, Auto Loan, Credit-Builder Loan, Credit-Builder Loan, Auto Loan, Not Specified, Credit-Builder Loan, and Debt Consolidation Loan3724.02.867.00.03536.4438.97872412 Years and 9 Months1.085.558903137.719750Low_spent_Medium_value_payments213.1349301
76598CUS_0x3a30752.0Developer29368.5602483.3800005432.0Credit-Builder Loan, and Auto Loan1816.06.924.02.0169.0738.42410317 Years and 3 Months0.039.979623125.070413Low_spent_Large_value_payments353.2879642
36385CUS_0x39ad231.0Mechanic63525.640NaN4793.0Debt Consolidation Loan, Student Loan, and Debt Consolidation Loan145.010.732.02.01016.0040.95211630 Years and 3 Months0.0145.929560325.917099Low_spent_Small_value_payments359.1336742
20814CUS_0x76c1729.0Writer66422.980NaN875NaNAuto Loan, Auto Loan, Debt Consolidation Loan, Payday Loan, Debt Consolidation Loan, Debt Consolidation Loan, and Not Specified2411.021.325.0NaN153.9932.37382811 Years and 4 Months1.0280.92755154.105898High_spent_Large_value_payments443.5913851